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在 7 特斯拉下使用 2D-EPI 和 3D-EPI 采集的 fMRI 数据中的信号波动。

Signal fluctuations in fMRI data acquired with 2D-EPI and 3D-EPI at 7 Tesla.

机构信息

Department of Bioengineering, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal.

出版信息

Magn Reson Imaging. 2013 Feb;31(2):212-20. doi: 10.1016/j.mri.2012.07.001. Epub 2012 Aug 24.

Abstract

Segmented three-dimensional echo planar imaging (3D-EPI) provides higher image signal-to-noise ratio (SNR) than standard single-shot two-dimensional echo planar imaging (2D-EPI), but is more sensitive to physiological noise. The aim of this study was to compare physiological noise removal efficiency in single-shot 2D-EPI and segmented 3D-EPI acquired at 7 Tesla. Two approaches were investigated based either on physiological regressors (PR) derived from cardiac and respiratory phases, or on principal component analysis (PCA) using additional resting-state data. Results show that, prior to physiological noise removal, 2D-EPI data had higher temporal SNR (tSNR), while spatial SNR was higher in 3D-EPI. Blood oxygen level dependent (BOLD) sensitivity was similar for both methods. The PR-based approach allowed characterization of relative contributions from different noise sources, confirming significant increases in physiological noise from 2D to 3D prior to correction. Both physiological noise removal approaches produced significant increases in tSNR and BOLD sensitivity, and these increases were larger for 3D-EPI, resulting in higher BOLD sensitivity in the 3D-EPI than in the 2D-EPI data. The PCA-based approach was the most effective correction method, yielding higher tSNR values for 3D-EPI than for 2D-EPI postcorrection.

摘要

分段三维 echo 平面成像(3D-EPI)比标准单次二维 echo 平面成像(2D-EPI)提供更高的图像信噪比(SNR),但对生理噪声更敏感。本研究旨在比较 7T 采集的单次 2D-EPI 和分段 3D-EPI 的生理噪声去除效率。研究了两种方法,一种基于来自心脏和呼吸相位的生理回归器(PR),另一种基于主成分分析(PCA)并使用额外的静息状态数据。结果表明,在进行生理噪声去除之前,2D-EPI 数据的时间 SNR(tSNR)更高,而 3D-EPI 的空间 SNR 更高。血氧水平依赖(BOLD)灵敏度在两种方法中相似。基于 PR 的方法可以对不同噪声源的相对贡献进行特征描述,在纠正之前证实了从 2D 到 3D 的生理噪声显著增加。两种生理噪声去除方法都显著提高了 tSNR 和 BOLD 灵敏度,对于 3D-EPI 来说,这些增加更大,导致 3D-EPI 的 BOLD 灵敏度高于 2D-EPI 数据。基于 PCA 的方法是最有效的校正方法,校正后 3D-EPI 的 tSNR 值高于 2D-EPI。

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